Stakeholder involvement in decision making: The development of a mass participation tool

Research output: ThesisPhD Thesis

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Abstract

In a decision-making problem, sometimes there is a need to involve multiple stakeholder groups because of different reasons. For example, a sustainability decision-making problem needs to account not only for economic development but also for environmental and social actions; it must involve the key stakeholder groups in decisions. Decision-making requires support from key stakeholder groups, and the stakeholder groups need to express their opinions because the decision outcome will have an impact on those groups in turn. Multi-criteria group decision-making (MCGDM) is a family of methods that takes into account conflicting criteria and simultaneously pursuing the inter-
ests of multiple stakeholder groups. However, when larger groups, such as citizens, are involved in the decision-making process, decision-making problems become more complicated. Thus, it may be insufficient to invite only representatives of stakeholder
groups, as conventional MCGDM does. In these cases, it might be more appropriate to establish a channel to hear from more voices in order to foster legibility and transparency in decision-making. A possible solution is to involve more participants in the decision-making process. I call it mass-participation decision-making. However, involving more participants also further increases the cost and complexity of the decision process and increases the number of potential conflicts among the participants due to
the larger number of participants. Furthermore, the assessments made by participants who do not receive guidance or lack expertise run a higher risk of inaccuracy. Therefore, I propose a new mass-participation decision-making framework, with
the intention to exploit the benefits of mass-participation decision-making and minimize the drawbacks because of higher participants. Among existing MCGDM frameworks, multi-actor multi-criteria analysis (MAMCA) is chosen as a foundation. It is an
MCGDM framework that allows different stakeholder groups to use different criteria to assess the performance of alternatives. The assessment results can be visualized in a single view that reflects the interests of different stakeholder groups. MAMCA is a
suitable framework to express the preferences of stakeholder groups. With MAMCA, stakeholder groups can be made aware of the interests of other groups, so as to have a better mutual understanding of their positions, thereby making them more prone to searching for compromised solutions and to possibly reach a consensus.
Thus far, MAMCA has not yet been applied in mass participation contexts. When citizens or other large groups are involved in MAMCA projects, the current practice is to invite their representatives to a workshop along with other stakeholder groups, where they elicit criteria weights and assess the performances of the alternatives. However, when a stakeholder group is strongly heterogeneous, i.e., when its members have different priorities regarding the criteria, it is questionable whether the chosen representatives can represent the groups’ interests. In these cases, the representation of the group by a
single appointed representative might lead to an unacceptable loss of information. Thus, the proposed mass-participation framework aims to provide a more legible procedure to increasing the level of representation; this is done by including more participants at different stages of the decision-making process when necessary, while also minimizing the negative effects when the number of participants increases. The process of the framework is executed as follows: first, the conventional MAMCA steps are
followed to structure the problems, define the alternatives, and identify key stakeholder groups. Then, the stakeholder groups that need mass participation are identified. Next, the criteria sets for stakeholder groups are then defined. Optionally, the facilitators can select criteria by soliciting the opinions from participants. Then, the participants in the mass participation groups respond to a survey to rank the criteria in order of importance, from highest to lowest. There is an extra step that needs to be taken if the participants hold diverse priorities, they are then clustered into subgroups according to their priorities, i.e., their ranking of the criteria. Then, using clustering analysis, representatives
from the subgroups are chosen and invited to a workshop for further assessment. Following the instructions of the facilitators, the stakeholder group representatives will elicit the criteria weights and assess the alternatives during the workshop. Lastly, the assessment results of the group are displayed. The representatives of groups can discuss to seek the final consensus.
This PhD dissertation presents both a framework and a tool for mass-participation. A theoretical framework alone is insufficient to facilitate the use of a mass participation MAMCA framework in a real-world decision-making problem; therefore, I enrich it as
a mass participation tool by developing a software that can perform mass-participation decision-making. I propose how the mass-participation decision-making is applied in
the framework. The framework itself consists of several methodologies to facilitate the decision-making: First, I propose a criteria pre-processing framework, that helps facilitators to select criteria for stakeholder groups by taking into account participants’
opinions. Then, a new clustering algorithm is developed that can cluster the participants into subgroups based on their priorities in the mass participation groups and identify the representatives of the subgroups; I also build a model to assist facilitators in reaching a possible consensus among stakeholder groups’ preferences by providing numerical references for the performance of the alternatives. Finally, this dissertation also includes the development of a dedicated software and a survey tool which facilitates mass-participation.
Original languageEnglish
Awarding Institution
  • Vrije Universiteit Brussel
Supervisors/Advisors
  • Te Boveldt, Geert, Supervisor
  • Macharis, Cathy, Supervisor
Award date5 Dec 2022
Publication statusPublished - 2022

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